In recent years the MLOps space is continuing to grow with more tools that are designed to make model building and training simpler, more automated and scalable. However, it’s not always easy to determine which MLOps tools answer your needs best.
What is Training Orchestration?
Training Orchestration enables data science and machine learning teams to run highly concurrent, scalable and maintainable training workflows.
With training orchestration tools, you can run your model training pipelines in the cloud instead of your local machine. This is especially useful for training processes that can take a long time, such as deep learning models.
Why Are Training Orchestration Tools Important?
Training orchestration tools allow your workflows and pipeline infrastructure to be automatically managed and simplified using a collaborative interface. By adopting training orchestration tools, ML teams are able to build, train, and deploy more models at scale.
The following list highlights relevant Training Orchestration tools and their benefits for data science and machine learning teams.
For a curated list of useful MLOps tools and projects to help you build your ML infrastructure – including training orchestration, data versioning, feature store, model monitoring and more, see our project: MLOps Toys.
An open-source deep learning training platform that enables data scientists to quickly and easily build their models.
- Uses advanced distributed training to train models faster – no need to change model code
- Hyperparameter tuning makes it easier to build models faster, at scale
- Smart scheduling & preemptible instances enable you to get more from GPUs and decrease cloud GPU costs
- Out-of-the-box experiment tracking to track and reproduce experiments
All of these features are integrated into a single user-friendly deep learning environment.
Easily build scalable production-grade orchestration for data and ML.
- Kubernetes-native workflow automation platform
- Ergonomic SDKs in Python, Java & Scala
- Versioned & Auditable
- Reproducible Pipelines
- Strong Data Typing
Kubeflow’s goal is to provide a simple, portable, and scalable way to deploy best-of-breed open-source systems for machine learning to diverse infrastructures.
A collaborative platform with a Unified UI to manage all data science activities in one place and introduce MLOps practice into the production systems of customers and developers. It is a collection of cloud-native tools for all of these stages of MLOps:
- Data exploration
- Feature preparation
- Model training/tuning
- Model serving, testing and versioning
Katonic is for both data scientists and data engineers looking to build production-grade machine learning implementations and can be run either locally in your development environment or on a production cluster. Katonic provides a unified system—leveraging Kubernetes for containerization and scalability for the portability and repeatability of its pipelines.
An open-source platform that provides complete AI model training and resource management capabilities.
- Easy to extend
- Supports on-prem, cloud, and hybrid environments
A platform to build data pipelines the easy way with no frameworks or YAML. Allows you to write your data processing code directly in Python, R, Julia or Bash.
- Visually construct pipelines through our user-friendly UI
- Code in Notebooks
- Run any subset of a pipeline directly or periodically
- Easily define your dependencies to run on any machine
A framework that develops and tests workflows locally, and then seamlessly executes them in a distributed environment.
- Cloud-agnostic, and running in AWS Batch, Airflow and Kubernetes
- Integrates with Jupyter, develops interactively, and deploys to the cloud without code changes
- Incremental builds; speeds up execution by skipping tasks whose source code has not changed
- Flexible by supporting functions, scripts, notebooks, and SQL scripts as tasks
- Parallelization by automatically parallelizing independent tasks
- Interactive console which helps debug workflows quickly
An open-source, pluggable MLOps platform that enables enterprises to develop, train, and deploy ML models at scale.
- Cluster Computing with multi-tenancy
- One-Click Notebook Environments
- Group-centric Datasets Management / Resources Management / Access-control Management
- Custom Machine Learning Environments with Image Builder
- Model Tracking and Deployment
- Capability Augmentation with 3rd-party Apps Store
A framework that helps manage complex parameter configurations that are defined by simple and familiar class-based structures. This allows Spock to support inheritance, read from multiple markdown formats, and allow hierarchical configuration by composition.
- Simple declaration, supports required/optional and automatic defaults
- Easily managed parameter groups, parameter inheritance
- Complex types, multiple configuration file types, hierarchical configuration
- Command-line overrides, immutable, tractability and reproducibility to save runtime parameter configuration
Stoke is a lightweight wrapper for PyTorch that provides a simple declarative API for context switching between devices, distributed modes, mixed-precision, and PyTorch extensions.
- Enables switching from local full-precision CPU to mixed-precision distributed multi-GPU with extensions
- Shows configuration settings for every underlying backend for those who want configurability and raw access to the underlying libraries
Valohai is an MLOps platform that handles machine orchestration, automatic reproducibility, and deployment.
- Technology agnostic, runs everything in Docker containers so you can run almost anything on it
- Runs on any cloud, natively supports Azure, AWS, GCP and OpenStack
- API, CLI, GUI and Jupyter integration to almost any workflow through its many interfaces
- Managed service by experienced DevOps engineers
An end-to-end deep learning platform that automates complex ML infrastructure and operational work required to train and deploy AI models. Spell is fully hybrid-cloud, and can deploy easily into any cloud or on-prem hardware.
- Automate cloud training execution from a user’s local CLI as a tracked and reproducible experiment, capturing all outputs and comprehensive metrics
- Serve models directly into production from a model registry, complete with lineage metadata, backed by a managed Kubernetes cluster for maximum scalability and robustness
- Manage, organize, collaborate on, and visualize your entire ML training portfolio in the cloud, under one centralized control pane.
Building an ML Infrastructure from Scratch
Any MLOps or data science team can create their own ML infrastructure given the right tools to support their needs. Want to see how it works in practice? Check out our live coding session: How to Build an ML Platform from Scratch with our CTO to get started in no time.